1 citations · 2 across the 5 of their papers we have counts for
5 papers
Audio-Enhanced Vision-Language Modeling with Latent Space Broadening for High Quality Data Expansion
Yu Sun, Yin Li, Ruixiao Sun +7
Transformer-based multimodal models are widely used in industrial-scale recommendation, search, and advertising systems for content understanding and relevance ranking. Enhancing l…
CLIPRerank: An Extremely Simple Method for Improving Ad-hoc Video Search
Aozhu Chen, Fangming Zhou, Ziyuan Wang +1
Ad-hoc Video Search (AVS) enables users to search for unlabeled video content using on-the-fly textual queries. Current deep learning-based models for AVS are trained to optimize h…
Lightweight Attentional Feature Fusion: A New Baseline for Text-to-Video Retrieval
Fan Hu, Aozhu Chen, Ziyue Wang +3
In this paper we revisit feature fusion, an old-fashioned topic, in the new context of text-to-video retrieval. Different from previous research that considers feature fusion only…
What Matters for Ad-hoc Video Search? A Large-scale Evaluation on TRECVID
Aozhu Chen, Fan Hu, Zihan Wang +2
For quantifying progress in Ad-hoc Video Search (AVS), the annual TRECVID AVS task is an important international evaluation. Solutions submitted by the task participants vary in te…
SEA: Sentence Encoder Assembly for Video Retrieval by Textual Queries
Xirong Li, Fangming Zhou, Chaoxi Xu +2
Retrieving unlabeled videos by textual queries, known as Ad-hoc Video Search (AVS), is a core theme in multimedia data management and retrieval. The success of AVS counts on cross-…